| In the research of remote sensing application, the very clear images after being processed play an important role in the following work. And image enhancement technology has a positive effect on improving the contrast and showing some local details of images, so it's good to reduce the effect of sensor, atmosphere etc on images.Traditional enhancement algorithms are detailedly studied and realized. Including linear transform, piecewise linear transform, non-linear transform, histogram equilibration. Furthermore, some corresponding improved algorithms based on clustering theory and fuzzy enhancement theory are put forward.Aiming to the key problem of how to divide gray scale intervals and fix on the points, a Remote Sensing Image Enhancement Algorithm Based on Weighting Fuzzy C-Means Clustering (RSIE-WFCM) is proposed. Image is divided into some areas quickly and these areas are enhanced by the relevant transform methods. The experimental results show the algorithm in this paper can enhance the signal noise ratio, contrast and definition of the image effectively, moreover, it is easy to realize.To solve some drawbacks of the traditional fuzzy enhancement algorithms, a Remote Sensing Image Fuzzy Enhancement Algorithm Based on Maximum Tsallis Entropy Principle (RSIFE-MTEP) is proposed. First, Tsallis entropy is generalized to the case of multi-level thresholds. The optimal gray thresholds are computed by genetic algorithm. Then, images are fuzzy enhanced with improved membership function and enhancement operator. Finaly, the fuzzy enhancement algorithm for processing the images with multi-level thresholds is generalized. The experimental results show that the proposed algorithm can select the thresholds automatically and efficiently, and achieve the better seeing effect, distributing brightness uniformly and enhancing image contrast distinctly.The experimental results show that the algorithms proposed by the paper achieve comparative good effects for some remote sensing images. |
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